Ananda Samajdar

1.6k total citations · 1 hit paper
17 papers, 941 citations indexed

About

Ananda Samajdar is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Ananda Samajdar has authored 17 papers receiving a total of 941 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Electrical and Electronic Engineering, 7 papers in Computer Vision and Pattern Recognition and 5 papers in Artificial Intelligence. Recurrent topics in Ananda Samajdar's work include Advanced Memory and Neural Computing (9 papers), Advanced Neural Network Applications (7 papers) and Neural Networks and Applications (4 papers). Ananda Samajdar is often cited by papers focused on Advanced Memory and Neural Computing (9 papers), Advanced Neural Network Applications (7 papers) and Neural Networks and Applications (4 papers). Ananda Samajdar collaborates with scholars based in United States, Canada and Germany. Ananda Samajdar's co-authors include Tushar Krishna, Hyoukjun Kwon, Eric Qin, Bharat Kaul, Sudarshan Srinivasan, Dipankar Das, Matthew Mattina, Yuhao Zhu, Jan Moritz Joseph and Paul N. Whatmough and has published in prestigious journals such as ACM SIGPLAN Notices, IEEE Micro and Rare & Special e-Zone (The Hong Kong University of Science and Technology).

In The Last Decade

Ananda Samajdar

16 papers receiving 927 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

SIGMA: A Sparse and Irregular GEMM Accelerator with Flexible Interconnects for DNN Training

2020 288 citations Eric Qin, Ananda Samajdar et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Ananda Samajdar United States	12	549	422	364	287	212	17	941
Jason Clemons United States	12	422 0.8×	301 0.7×	267 0.7×	166 0.6×	172 0.8×	21	758
Hardik Sharma United States	6	518 0.9×	493 1.2×	280 0.8×	362 1.3×	139 0.7×	13	918
Brian Zimmer United States	18	845 1.5×	262 0.6×	416 1.1×	188 0.7×	195 0.9×	49	1.2k
Liqiang Lu China	13	546 1.0×	647 1.5×	314 0.9×	306 1.1×	118 0.6×	38	1.1k
Ben Keller United States	16	660 1.2×	232 0.5×	385 1.1×	154 0.5×	163 0.8×	28	941
Shengen Yan China	14	332 0.6×	495 1.2×	371 1.0×	252 0.9×	285 1.3×	32	946
Jorge Albericio Canada	12	664 1.2×	677 1.6×	433 1.2×	414 1.4×	237 1.1×	20	1.2k
Patrick Judd Canada	10	674 1.2×	800 1.9×	283 0.8×	480 1.7×	106 0.5×	15	1.1k
Debbie Marr United States	12	483 0.9×	471 1.1×	298 0.8×	350 1.2×	191 0.9×	17	989
Tong Geng United States	18	339 0.6×	368 0.9×	289 0.8×	398 1.4×	213 1.0×	70	951

Countries citing papers authored by Ananda Samajdar

Since Specialization

Citations

This map shows the geographic impact of Ananda Samajdar's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ananda Samajdar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ananda Samajdar more than expected).

Fields of papers citing papers by Ananda Samajdar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ananda Samajdar. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ananda Samajdar. The network helps show where Ananda Samajdar may publish in the future.

Co-authorship network of co-authors of Ananda Samajdar

This figure shows the co-authorship network connecting the top 25 collaborators of Ananda Samajdar. A scholar is included among the top collaborators of Ananda Samajdar based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ananda Samajdar. Ananda Samajdar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

17 of 17 papers shown

1.

Wan, Zishen, et al.. (2025). CogSys: Efficient and Scalable Neurosymbolic Cognition System via Algorithm-Hardware Co-Design. 775–789. 2 indexed citations

2.

Wan, Zishen, et al.. (2025). NSFlow: An End-to-End FPGA Framework with Scalable Dataflow Architecture for Neuro-Symbolic AI. 1–7.

3.

Wan, Zishen, Chaojian Li, Haoran You, et al.. (2024). Towards Efficient Neuro-Symbolic AI: From Workload Characterization to Hardware Architecture. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1(1). 53–68. 7 indexed citations

4.

Wan, Zishen, Chaojian Li, Haoran You, et al.. (2024). Towards Cognitive AI Systems: Workload and Characterization of Neuro-Symbolic AI. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 268–279. 14 indexed citations

5.

Samajdar, Ananda, Eric Qin, Michael Pellauer, & Tushar Krishna. (2022). Self adaptive reconfigurable arrays (SARA). Proceedings of the 59th ACM/IEEE Design Automation Conference. 583–588. 14 indexed citations

6.

Qin, Eric, Ananda Samajdar, Christopher J. Hughes, et al.. (2021). RASA: Efficient Register-Aware Systolic Array Matrix Engine for CPU. 253–258. 11 indexed citations

7.

Samajdar, Ananda, Jan Moritz Joseph, Yuhao Zhu, et al.. (2020). A Systematic Methodology for Characterizing Scalability of DNN Accelerators using SCALE-Sim. 58–68. 119 indexed citations

8.

Krishna, Tushar, Hyoukjun Kwon, Angshuman Parashar, Michael Pellauer, & Ananda Samajdar. (2020). Data Orchestration in Deep Learning Accelerators. 15(3). 1–164. 6 indexed citations

9.

Krishna, Tushar, Hyoukjun Kwon, Angshuman Parashar, Michael Pellauer, & Ananda Samajdar. (2020). Data Orchestration in Deep Learning Accelerators. 6 indexed citations

10.

Qin, Eric, Ananda Samajdar, Hyoukjun Kwon, et al.. (2020). SIGMA: A Sparse and Irregular GEMM Accelerator with Flexible Interconnects for DNN Training. 58–70. 288 indexed citations breakdown →

11.

Samajdar, Ananda, Tushar Garg, Tushar Krishna, & Nachiket Kapre. (2019). Scaling the Cascades: Interconnect-Aware FPGA Implementation of Machine Learning Problems. 342–349. 17 indexed citations

12.

Samajdar, Ananda, et al.. (2018). SCALE-Sim: Systolic CNN Accelerator.. arXiv (Cornell University). 44 indexed citations

13.

Kwon, Hyoukjun, Ananda Samajdar, & Tushar Krishna. (2018). MAERI. 461–475. 199 indexed citations

14.

Kwon, Hyoukjun, Ananda Samajdar, & Tushar Krishna. (2018). A Communication-Centric Approach for Designing Flexible DNN Accelerators. IEEE Micro. 38(6). 25–35. 12 indexed citations

15.

Kwon, Hyoukjun, Ananda Samajdar, & Tushar Krishna. (2018). MAERI. ACM SIGPLAN Notices. 53(2). 461–475. 136 indexed citations

16.

Samajdar, Ananda, et al.. (2018). GeneSys: Enabling Continuous Learning through Neural Network Evolution in Hardware. 855–866. 16 indexed citations

17.

Kwon, Hyoukjun, Ananda Samajdar, & Tushar Krishna. (2017). Rethinking NoCs for Spatial Neural Network Accelerators. 1–8. 50 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact